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Retail Data Analytics for one store one year
Python notebook using data from Retail Data Analytics · 1,267 views · 2y ago
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Input
13.22 MB
folder

Data Sources

      arrow_drop_down

      Retail Data Analytics

      Retail Data Analytics
          calendar_view_week

          Features data set.csv

          Features data set.csv (586.4 KB)
          calendar_view_week

          sales data-set.csv

          sales data-set.csv (12.65 MB)
          calendar_view_week

          stores data-set.csv

          stores data-set.csv (577 B)
Features data set.csv(586.4 KB)
get_app
Download

10 of 12 columns

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About this file

Contains additional data related to the store, department, and regional activity for the given dates.

grid_3x3Storesort
text_formatDatesort
grid_3x3Temperaturesort
grid_3x3Fuel_Pricesort
text_formatMarkDown1sort
text_formatMarkDown2sort
text_formatMarkDown3sort
text_formatMarkDown4sort
text_formatMarkDown5sort
text_formatCPIsort
LabelCount
1.00 - 5.40910
5.40 - 9.80728
9.80 - 14.20910
14.20 - 18.60728
18.60 - 23.00728
23.00 - 27.40910
27.40 - 31.80728
31.80 - 36.20910
36.20 - 40.60728
40.60 - 45.00910
1
45
182
unique values
LabelCount
-7.29 - 3.637
3.63 - 14.5647
14.56 - 25.48282
25.48 - 36.41761
36.41 - 47.331,130
47.33 - 58.251,492
58.25 - 69.181,676
69.18 - 80.101,560
80.10 - 91.031,073
91.03 - 101.95162
-7.29
102
LabelCount
2.47 - 2.67424
2.67 - 2.871,016
2.87 - 3.07743
3.07 - 3.27778
3.27 - 3.47857
3.47 - 3.671,800
3.67 - 3.871,502
3.87 - 4.07800
4.07 - 4.27222
4.27 - 4.4748
2.47
4.47
NA51%
NA
4855.310%
4855.31
Other (4030)49%
NA64%
NA
30%
3
Other (2910)36%
NA56%
NA
10%
1
Other (3596)44%
NA58%
NA
30%
3
Other (3459)42%
NA51%
NA
986.230%
986.23
Other (4048)49%
NA7%
NA
132.71609680%
132.7160968
Other (7572)92%
1
05/02/2010
42.31
2.572
NA
NA
NA
NA
NA
211.0963582
1
12/02/2010
38.51
2.548
NA
NA
NA
NA
NA
211.2421698
1
19/02/2010
39.93
2.514
NA
NA
NA
NA
NA
211.2891429
1
26/02/2010
46.63
2.561
NA
NA
NA
NA
NA
211.3196429
1
05/03/2010
46.5
2.625
NA
NA
NA
NA
NA
211.3501429
1
12/03/2010
57.79
2.667
NA
NA
NA
NA
NA
211.3806429
1
19/03/2010
54.58
2.72
NA
NA
NA
NA
NA
211.215635
1
26/03/2010
51.45
2.732
NA
NA
NA
NA
NA
211.0180424
1
02/04/2010
62.27
2.719
NA
NA
NA
NA
NA
210.8204499
1
09/04/2010
65.86
2.77
NA
NA
NA
NA
NA
210.6228574
1
16/04/2010
66.32
2.808
NA
NA
NA
NA
NA
210.4887
1
23/04/2010
64.84
2.795
NA
NA
NA
NA
NA
210.4391228
1
30/04/2010
67.41
2.78
NA
NA
NA
NA
NA
210.3895456
1
07/05/2010
72.55
2.835
NA
NA
NA
NA
NA
210.3399684
1
14/05/2010
74.78
2.854
NA
NA
NA
NA
NA
210.3374261
1
21/05/2010
76.44
2.826
NA
NA
NA
NA
NA
210.6170934
1
28/05/2010
80.44
2.759
NA
NA
NA
NA
NA
210.8967606
1
04/06/2010
80.69
2.705
NA
NA
NA
NA
NA
211.1764278
1
11/06/2010
80.43
2.668
NA
NA
NA
NA
NA
211.4560951
1
18/06/2010
84.11
2.637
NA
NA
NA
NA
NA
211.4537719
1
25/06/2010
84.34
2.653
NA
NA
NA
NA
NA
211.3386526
1
02/07/2010
80.91
2.669
NA
NA
NA
NA
NA
211.2235333
1
09/07/2010
80.48
2.642
NA
NA
NA
NA
NA
211.108414
1
16/07/2010
83.15
2.623
NA
NA
NA
NA
NA
211.1003854
1
23/07/2010
83.36
2.608
NA
NA
NA
NA
NA
211.2351443
1
30/07/2010
81.84
2.64
NA
NA
NA
NA
NA
211.3699032
1
06/08/2010
87.16
2.627
NA
NA
NA
NA
NA
211.5046621
1
13/08/2010
87
2.692
NA
NA
NA
NA
NA
211.6394211
1
20/08/2010
86.65
2.664
NA
NA
NA
NA
NA
211.6033633
1
27/08/2010
85.22
2.619
NA
NA
NA
NA
NA
211.5673056
1
03/09/2010
81.21
2.577
NA
NA
NA
NA
NA
211.5312479
1
10/09/2010
78.69
2.565
NA
NA
NA
NA
NA
211.4951902
1
17/09/2010
82.11
2.582
NA
NA
NA
NA
NA
211.5224596
1
24/09/2010
80.94
2.624
NA
NA
NA
NA
NA
211.5972246
1
01/10/2010
71.89
2.603
NA
NA
NA
NA
NA
211.6719895
1
08/10/2010
63.93
2.633
NA
NA
NA
NA
NA
211.7467544
1
15/10/2010
67.18
2.72
NA
NA
NA
NA
NA
211.8137436
1
22/10/2010
69.86
2.725
NA
NA
NA
NA
NA
211.8612937
1
29/10/2010
69.64
2.716
NA
NA
NA
NA
NA
211.9088438
1
05/11/2010
58.74
2.689
NA
NA
NA
NA
NA
211.9563939
1
12/11/2010
59.61
2.728
NA
NA
NA
NA
NA
212.003944
1
19/11/2010
51.41
2.771
NA
NA
NA
NA
NA
211.8896737
1
26/11/2010
64.52
2.735
NA
NA
NA
NA
NA
211.7484333
1
03/12/2010
49.27
2.708
NA
NA
NA
NA
NA
211.607193
1
10/12/2010
46.33
2.843
NA
NA
NA
NA
NA
211.4659526
1
17/12/2010
49.84
2.869
NA
NA
NA
NA
NA
211.4053124
1
24/12/2010
52.33
2.886
NA
NA
NA
NA
NA
211.4051222
1
31/12/2010
48.43
2.943
NA
NA
NA
NA
NA
211.4049321
1
07/01/2011
48.27
2.976
NA
NA
NA
NA
NA
211.4047419
1
14/01/2011
35.4
2.983
NA
NA
NA
NA
NA
211.4574109
Execution Info
Succeeded
True
Exit Code
0
Used All Space
False
Environment
Container Image (Dockerfile)
Run Time
90.2 seconds
Timeout Exceeded
False
Output Size
0
Accelerator
None
TimeLine #Log Message
3.2s1[NbConvertApp] Converting notebook __notebook__.ipynb to notebook
3.3s2[NbConvertApp] Executing notebook with kernel: python3
47.1s319/04/02 06:34:21 WARN NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
47.3s4Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties
47.3s5Setting default log level to "WARN". To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
53.7s6 [Stage 0:> (0 + 1) / 1]
54.1s7 [Stage 0:===========================================================(1 + 0) / 1]
54.2s8
56.1s9 [Stage 3:> (0 + 1) / 1]
56.5s10
...
76.9s29 [Stage 32:================================> (122 + 1) / 200]
77.1s30 [Stage 32:========================================> (151 + 2) / 200]
77.3s31 [Stage 32:================================================> (181 + 1) / 200]
77.4s32
78.5s33 [Stage 34:> (0 + 1) / 1]
79.2s34
86.7s35[NbConvertApp] Writing 123627 bytes to __notebook__.ipynb
89.0s36[NbConvertApp] Converting notebook __notebook__.ipynb to html
89.5s37[NbConvertApp] Support files will be in __results___files/ [NbConvertApp] Making directory __results___files
89.5s38[NbConvertApp] Making directory __results___files [NbConvertApp] Writing 293192 bytes to __results__.html
89.5s39
89.5s40...
Complete. Exited with code 0.

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